NHL

Alright, we’re only a little bit sorry we made you read our methodology post first, because we know what you really want is below. Still, we recommend you understand how we came to our ratings before you continue reading this post.

We’re sure you’ll disagree with us on some points, and that’s fine – despite our best efforts, these are still fairly subjective ranks. Still, try this exercise for yourself, and it’s possible your opinions will change.

It’s a hard question, plagued by subjectivity, by bias, and by lack of transparency. It’s complicated by league mandates like the expansion draft and the hard salary cap. It mixes the weight of process, results, and vision into one big stew, where it can be difficult to distinguish the meat from the sauce.

It’s a question, that unlike many others, is difficult to quantify with even the most advanced of stats.

And it’s one that the league has no desire to answer definitively, as that would only hurt the men currently in those roles.

Fortunately for you, Hockey Graphs loves tackling the hard questions.

In the following articles, we will attempt to rank all 31 of the NHL’s GMs, as objectively as possible, according to seven important criteria. They each painstakingly researched trade histories, draft selections, and salary cap management, coming up with a final score for each.

While this process still was subjective, in that these scores are not quantitatively derived, it was an extremely holistic process, and both of us were forced to confront some of our own biases.

Recently, the statistical analyst of an NHL team was let go in the aftermath of an underwhelming regular season and a puzzling decision involving one of the team’s most productive and iconic players. Rights and wrongs aside, the episode illustrated an uncomfortable fact: the analyst’s job is perhaps the most fragile one of all.

Imagine the tightrope walker, balancing him/herself atop a fine metal wire between two buildings. The job is a difficult one on the best of days, requiring a lifetime of practice and undivided focus. Randomness is not the tightrope walker’s friend. A gust of wind, a slight mis-step or even a meeting with an errand low-flying pigeon could yield deadly consequences.

While the physical stakes are different, an analyst’s career prospects (and personal well-being) are similarly affected by things out of his or her control. While job security in any field is dependent on market conditions, things are especially dire for the technical worker responsible for uncovering Truths, but ranked too low in the corporate hierarchy to effect real change.

Something I set time aside for during the off-season is reading non-hockey books in an attempt to gain a better perspective on hockey. The work of Michael Lewis (Liar’s Poker, The Big Short, Boomrang) and Nassim Taleb (The Black Swan, Fooled By Randomness) were of particular inspiration.

Below are some assorted thoughts based on recent readings and events. Tweet me (@ML_Han) if you’d like to disagree and tell me why. Eventually I hope to spend some time talking about this or a tangential at the second edition of RITHAC this September.

On Monday, fans tuned into one of the quieter NHL trade deadlines in recent memory. Despite the slow pace of movements, Matt Cane (@cane_matt) and I set about making visuals to give our takes on each trade.

Here, we’ll look back on a few of our takes from the trade deadline. We’ll focus ourselves with three categories – a trade where we had a similar take, a trade we disagreed on, and our favourite viz from the day.

Despite them accounting for approximately 20 percent of NHL game time, special teams have been largely ignored when it comes to analytics. Considering the data available and its small sample size compared to even-strength, that is somewhat understandable, and there have certainly been attempts to properly quantify and assess power plays. So what do we know so far? Continue reading →

Long has it been argued that sustained zone time is a reliable way to not only prevent your opponents from scoring but as a way to produce offense of your own. The argument that is often made, or at least the one that’s often heard, is that the longer you are in the offensive zone the more likely it is that the defense will become fatigued and make a mistake that leaves someone open for a prime scoring opportunity.

So let’s test that theory by asking a more data driven question; does sustained zone time lead to an increase in shooting percentage?

Whether you come at hockey from the numbers or from traditional scouting, finding NHL-quality goaltending is a challenge. In order to have a good sense of a goalie’s talent (as measured by even-strength Sv%), you need to observe about 4,000 shots worth of work. On average, a goalie needs to play over three seasons as a starter (or eight seasons as a backup) to see that many shots. If they play poorly, few netminders will ever get close to that amount of playing time and most goalies are entering age-related decline by the time they’ve seen that many shots. As such, teams usually make decisions on goaltenders long before they’ve seen 4,000 shots and, unsurprisingly, teams make mistakes.

Over the summer, the NHL made a number of significant rule changes to make the game more entertaining to fans and more fair for teams, with 3-on-3 overtime being the most revolutionary and thus far the most applauded.

Buried down at the bottom of the list of rule changes, however, was a much less significant note. It involved faceoffs – you know, that thing data analysts get peeved at commentators for overemphasizing. For years, the standard procedure has been that the visiting team’s player is required to put his blade on the ice prior to his opponent. This is an advantage for the home player, as he can attempt to secure the puck back to his side with one consistent motion rather than having to move his stick forward and then backward.

Expected goals models have been developed in a number of sports to better predict future performance. For sports like hockey and soccer where goals are inherently random and scarce, expected goals models proved to be particularly useful at predicting future scoring. This is because they take into account shot attempts, which are better predictors of a team and player’s performance than goal totals alone.

A notable example is Brian Macdonald’s expected goals model dating back to 2012, which used shot differentials (Corsi, Fenwick) and other variables like faceoffs, zone starts and hits. Important developments have been made since then in regards to the predictive value of those variables, particularly those pertaining to shot quality.

Shot quality has been the subject of spirited debate despite evidence suggesting that it plays an important role in predicting goals. The evidence shows that shot characteristics like distance and angle can significantly influence the probability of a certain shot resulting in a goal. Previous attempts to account for shot quality in an expected goals model format have been conducted by Alan Ryder, see here and here.

In Part I, an updated expected goals (xG) model will be presented that accounts for shot quality and a number of other variables. Part II will deal with testing the performance of xG against previous models like score-adjusted Corsi and goals percentage.